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Certified AI Engineer Masterclass: Build AI Agents 2026
1,559 students
Certified AI Engineer Masterclass: Build AI Agents 2026
Build real AI systems, LLM apps, RAG, agents & deploy on AWS — from beginner to advanced
Created byData Science Academy, School of AI
Last updated 4/2026
English
English [Auto],
What you'll learn
- Build real-world AI applications using Large Language Models (GPT, Claude, etc.)
- Master Prompt Engineering techniques (zero-shot, few-shot, structured outputs)
- Develop AI Agents with memory, tools, and automation workflows
- Implement Retrieval-Augmented Generation (RAG) using embeddings and vector databases
- Integrate AI into applications using APIs (Python & JavaScript)
- Design and build full-stack AI systems (frontend + backend)
- Deploy AI applications using AWS, Docker, and modern DevOps practices
- Optimize AI systems for cost, latency, and performance
- Understand and mitigate AI risks, security issues, and bias
Explore related topics
Course content
15 sections • 71 lectures • 12h 35m total length
Requirements
- No prior experience in AI or Machine Learning is required — this course starts from the basics
- Basic computer skills (installing software, using a browser, managing files)
- A laptop or desktop with a stable internet connection
Description
“This course contains the use of artificial intelligence”
This is not just another AI course — this is a complete AI Engineer roadmap designed to take you from zero to building real-world AI systems.
In this masterclass, you won’t just learn theory — you’ll build, deploy, and scale AI applications using the most in-demand technologies in the industry today.
You’ll start by understanding the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning, then quickly move into hands-on Python, where you’ll learn how to work with data and build reusable code.
From there, you’ll dive deep into Generative AI and Large Language Models (LLMs) — including how models like GPT and Claude work, and how to control them using advanced prompt engineering techniques.
But we don’t stop there.
You’ll learn how to:
Build AI-powered applications using APIs
Implement Retrieval-Augmented Generation (RAG) systems
Create AI Agents that can think, act, and automate tasks
Design multi-agent systems that collaborate like real teams
Develop full-stack AI applications
Deploy your projects using AWS, Docker, and modern DevOps practices
You’ll also learn how to optimize costs, reduce latency, and secure AI systems — skills that separate beginners from real engineers.
By the end of this course, you will be able to:
Build real AI products
Deploy them to production
Understand how AI systems work end-to-end
Position yourself as a job-ready AI Engineer
This course is designed with a project-first approach, ensuring you gain practical, real-world experience — not just theoretical knowledge.
Who this course is for:
- Beginners who want to become AI Engineers and build real-world AI systems from scratch
- Developers (frontend/backend) looking to transition into AI, LLMs, and automation
- Data Analysts / Data Engineers who want to move into Generative AI and advanced applications
- Students and fresh graduates preparing for careers in AI, Machine Learning, or software engineering
- Entrepreneurs and startup builders who want to create AI-powered products or automate workflows
- Professionals interested in AI Agents, RAG systems, and modern AI architectures